Sparse Bayesian learning-based robust STAP algorithm

被引：3

作者：

Li Z. ^{[1
]}

Wang T. ^{[1
]}

机构：

[1] National Laboratory of Radar Signal Processing, Xidian University, Xi'an

来源：

Xi Tong Gong Cheng Yu Dian Zi Ji Shu/Systems Engineering and Electronics | 2023年 / 45卷 / 10期

关键词：

array gain/phase errors; grid mismatches; space-time adaptive processing (STAP); sparse Bayesian learning;

D O I：

10.12305/j.issn.1001-506X.2023.10.05

中图分类号：

学科分类号：

摘要：

To improve the performance of sparse recovery space-time adaptive processing (SR-STAP) algorithms with both array gain/phase errors and grid mismatches, a sparse Bayesian learning-based robust SR-STAP approach is proposed in this paper. Firstly, the SR-STAP signal model with mismatched errors is constructed using the Kronecker structure of the space-time steering vector. Secondly, the angle-Doppler profile and mismatched parameters are alternatively achieved by utilizing the Bayesian inference and expectation-maximization algorithm. Finally, the precise clutter-plus-noise covariance matrix is estimated and the corresponding weight vector is calculated with the above obtained parameters. Simulation results verify that the proposed algorithm can significantly improve the target detection performance with mismatched sparse signal model. © 2023 Chinese Institute of Electronics. All rights reserved.

引用

页码：3032 / 3040

页数：8

共 26 条

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